<<<<<<< Updated upstream ======= >>>>>>> Stashed changes ALA Labs - Quantifying species range and overlap with fire-burned areas using concave hulls <<<<<<< Updated upstream ======= >>>>>>> Stashed changes <<<<<<< Updated upstream ======= :root { --primary-orange: #E06E53; --secondary-orange: #B8573E; --main-font: 'Roboto', sans-serif; --subheader-font: 'Roboto', sans-serif; --mono-font: 'Fira Mono', monospace; --header-font: 'Lato' sans-serif; } @media screen and (max-width: 952px){ .navbar-brand.navbar-brand-logo { background-attachment: scroll; content: 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); height: 50px; margin-top: -13px; margin-right: -5px; } } .navbar-brand>img { max-height: 48px; width: auto; padding-left: 9px; padding-top: 0px; padding-bottom: 0px; padding-right: 20px; } .navbar { padding-top: 1rem; padding-bottom: 1rem; } a.nav-link, a.nav-link:after, a.nav-link:before { transition: all .3s; } a.nav-link { position: relative; } .navbar-dark .navbar-nav .nav-link:hover { color: var(--primary-orange) !important; } .navbar-title:hover { color: var(--primary-orange) !important; } .navbar-title { display: inline-block; padding: 0; content: 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.paper-main-column { width: 100%; display: block; padding-top:20px; padding-bottom:20px; margin-left: auto; margin-right: auto; } .col-image { display: flex; justify-content: center; } } >>>>>>> Stashed changes

Quantifying species range and overlap with fire-burned areas using concave hulls

Calculating range overlap is an efficient way to estimate the impact of natural disasters on biodiversity. Here we’ll use curated datasets to compute concave hulls to visualise the spatial distribution of Apidae (Bees) and Daviesia (Bitterpeas) and their overlap with burned areas of the Black Summer fires of 2019-2020.

Eukaryota
Animalia
Plantae
Summaries
Maps
Authors

Fonti Kar

Margot Schneider

Published

April 11, 2023

Author

Fonti Kar
Margot Schneider

Date

11 April 2023

<<<<<<< Updated upstream

=======

>>>>>>> Stashed changes

The 2019/2020 Australian bushfires had a devastating impact on the natural landscape, threatening our native biodiversity. More than ever, decision makers need curated, open access biodiversity data to help respond effectively to future bushfires.

Our team at the Atlas of Living Australia (ALA) has been working with Invertebrates Australia and CSIRO National Research Collections team to collate biodiversity datasets that can be used for off-the-shelf bushfire assessments. The two datasets contain data on Australian taxonomic groups that are often overlooked and severely affected during bushfires: invertebrates (insects, molluscs, spiders) and vascular plants.

We are thrilled to announce that these datasets are available from CSIRO’s data access portal!

This post expands on our last post about using convex and alpha hulls to visualise distributions of data-deficient species.

Here, we show you how to compute a new form of spatial polygon — a concave hull — and use it to represent a species’ range and to calculate the overlap with fire-burned areas (range overlap). Unlike convex hulls, concave hulls have the added flexibility to adjust their tightness to the data (or concavity). This flexibility allows more accurate estimation of species ranges, making it a useful approach to rapidly assess how natural disasters like bush fires affected biodiversity.

Download data

First we will load the R packages we need:

# install.packages("pacman")
pacman::p_load(tidyverse, here, rmapshaper, sf, ggpointdensity, viridis, ozmaps, concaveman, cowplot, patchwork)

Next, we will go to the Data Access Portal to download the invertebrate and vascular plant datasets.

Click on the Files tab (under the main title), then the Download button (in the top right corner), and select Download all files as Zip archive. Save this zip file in a local folder inside your current R project and be sure to unzip it.

Now we can read the curated datasets into R:

# Invertebrates data
inverts <- read_csv(here("your_directory_name", "invertebrate.data.03.2023.csv"))

# Vascular plants data
vplants <- read_csv(here("your_directory_name", "vascularplant.data.03.2023.csv"))  |> 
  rename(latitude = latitude_used, # Rename coordinate variables for consistency
         longitude = longitude_used)

Overview of data

Both datasets are based on studies that investigated the impact of the Black Summer bushfires and are designed to support future modelling and impact assessments. The invertebrate dataset spans across Australia, whereas the vascular plant dataset is restricted to South-Eastern Australia and contains only species where more than 50% of their range was affected by the 2019/2020 fires.


Summary of Invertebrate and Vascular Plant Data

Taxonomic Group Classes Families Species Records
Invertebrates 46 2,044 44,146 300,987
Vascular Plants 7 76 896 41,572

A total of 44,146 invertebrate species and 896 species of vascular plants are represented in these datasets. Below, we’ve attempted to show the geographic range of these data. So much data makes it challenging to visualise all data points and concave hulls at once. As such, we created the concave hull maps (left) below by randomly selecting one invertebrate species from each class and one plant species from each family.

Code
# Identify species that have more than 4 observations 
more_than_4_obs <- inverts |> 
  group_by(scientific_name) |> 
  summarise(n_obs = n()) |> 
  filter(n_obs > 4) |> 
  pull(scientific_name)

# Subset species with more than 4 observations and appear on mainland Australia + Tasmania
inverts_subset <- inverts |>
  filter(scientific_name %in% more_than_4_obs) |> 
  filter(latitude < -10, latitude >= -45,
         longitude >= 113, longitude <= 155) |> 
  select(scientific_name:family, longitude, latitude)

# Nest occurrence data
inverts_nest <- inverts_subset |> 
  nest(coords = c(longitude, latitude))

# Subset a random species from each class 
set.seed(123)  # Set seed so we all get the same results
subset <- inverts_nest |> 
  group_by(class) |> 
  slice_sample(n = 1) 

# Convert coordinates into sf object and compute concave hulls as list columns.
subset_concave <- subset |>
    mutate(points_sf = map(.x = coords,
                           ~ st_as_sf(.x, coords = c("longitude", "latitude"),
                                      crs = 4326)), 
           concave_sf = map(points_sf,
                            ~ concaveman(.x)))

# Unnest the concave hull list column
subset_concave <- subset_concave |> 
  select(scientific_name:family, concave_sf) |> 
  unnest(cols = c(concave_sf)) |> 
  ungroup() |> 
  st_sf(crs = 4326) 

# Retrieve Australia polygon
aus <- st_transform(ozmap_country, 4326)

# Plotting spatial distributions
inverts_concave <- ggplot() + 
  geom_sf(data = aus, colour = "black", fill = NA) +
  geom_sf(data = subset_concave, fill = "#609966", alpha = 0.2, lwd = 0) +
  coord_sf(xlim = c(110, 155)) +
  theme_void() 

# Create plot showing overlapping points
inverts_points_map <- ggplot() +
  geom_pointdensity(data = inverts_subset,
                    mapping = aes(x = longitude,
                                  y = latitude)) +
  geom_sf(data = aus, colour = "white", fill = NA) +  
  scale_color_viridis(option = "E", begin = 0.1) +
  coord_sf(xlim = c(110, 155)) +
  guides(alpha = "none",
         colour = guide_colorbar(title = "Number of \noverlapping points")) +
  theme_void() +
  theme(legend.position = "bottom",
        legend.margin = margin(0, 0, 0, 0),
        legend.box.margin = margin(0, 0, 0, 0),
        legend.justification = "left"
        )

inverts_concave + inverts_points_map + plot_annotation(title = "Invertebrate Dataset") 

Code
# Identify species that have more than 4 observations 
more_than_4_obs_plants <- vplants |> 
  group_by(scientific_name) |> 
  summarise(n_obs = n()) |> 
  filter(n_obs > 4) |> 
  pull(scientific_name)

# Subset species with more than 4 observations and appear on mainland Australia + Tasmaina
vplant_subset <- vplants |>
  filter(scientific_name %in% more_than_4_obs_plants) |> 
  filter(latitude < -10, latitude >= -45,
         longitude >= 113, longitude <= 155) |> 
  select(species, class:genus, longitude, latitude) 

# Nest occurrence data
vplant_nest <- vplant_subset |> 
   nest(coords = c(longitude, latitude))

# Subset a random species from each family 
set.seed(123)  # Set seed so we all get the same results
plant_subset <- vplant_nest |> 
  group_by(family) |> 
  slice_sample(n = 1) 

# Compute concave hulls
pl_subset_concave <- plant_subset |>
    mutate(points_sf = map(.x = coords,
                           ~ st_as_sf(.x, coords = c("longitude", "latitude"),
                                      crs = 4326)), 
           concave_sf = map(points_sf,
                            ~ concaveman(.x)))

# Unnest the data
pl_subset_concave <- pl_subset_concave |> 
  select(species:family, concave_sf) |> 
  unnest(cols = c(concave_sf)) |> 
  st_as_sf(crs = 4326) 

# Plotting spatial distributions
plant_concave <- ggplot() + 
  geom_sf(data = aus, colour = "black", fill = NA) +
  geom_sf(data = pl_subset_concave, fill = "#609966", colour = NA, alpha = 0.15, lwd = 0) + 
  coord_sf(xlim = c(140, 158),
           ylim = c(-23, -43)) +
  theme_void()

# Create plot showing overlapping points
plants_points_map <- ggplot() +
  geom_pointdensity(data = vplant_subset,
                    mapping = aes(x = longitude,
                                  y = latitude)) +
  geom_sf(data = aus, colour = "black", fill = NA) +  
  scale_color_viridis(option = "E", begin = 0.1) +
  coord_sf(xlim = c(140, 158),
           ylim = c(-23, -43)) +
  guides(alpha = "none",
         colour = guide_colorbar(title = "Number of \noverlapping points")) +
  theme_void() +
  theme(legend.position = "bottom",
        legend.margin = margin(0, 0, 0, 0),
        legend.box.margin = margin(0, 0, 0, 0),
        legend.justification = "left"
        )
  
plant_concave + plants_points_map + plot_annotation(title = "Vascular Plant Dataset")

Pre-cleaning

Let’s use these datasets to calculate concave hulls and range overlaps with burned regions. One benefit of using these curated datasets is that they do not contain any duplicates or missing values. This makes data cleaning an easier job!

However, there are still a few steps we need to do before computing concave hulls:

Remove data-deficient species

First, we need to filter out any data-deficient species with fewer than 4 data points because concave hulls are best estimated with at least 4 data points. To do this, we’ll calculate the number of observations for each species, then identify which ones have more than 4 records. Using this list of species, we can extract their data.

more_than_4_obs <- inverts |> 
  group_by(scientific_name) |> 
  summarise(n_obs = n()) |> 
  filter(n_obs > 4) |> 
  pull(scientific_name)

more_than_4_obs |> head()
[1] "Aaaaba fossicollis"    "Aaaaba nodosus"        "Aades cultratus"      
[4] "Aades griseatus"       "Aaroniella rawlingsi"  "Abantiades latipennis"
inverts_subset <- inverts |>
  filter(scientific_name %in% more_than_4_obs)

Restrict data to mainland Australia and Tasmania

The invertebrate dataset includes records on offshore islands which can drastically skew the shape of a species’ concave hull. For the purpose of this post, we will filter these out and only use records that occur on mainland Australia and Tasmania.

subset_mainland <- inverts_subset |> 
  filter(latitude < -10, latitude >= -45,
         longitude >= 113, longitude <= 155) |> 
  select(scientific_name:family, longitude, latitude)

List columns and nesting occurrence data

For the majority of calculations in this post, we will be making use of list columns, a very useful data structure for iterative analyses. You can think of a list column as mini data frames nested within a column by a grouping variable.

In this case we are nesting the coordinate data by species, which will make operations at the species level more efficient.

inverts_nest <- subset_mainland |> 
  nest(coords = c(longitude, latitude))

inverts_nest |> 
  print(n = 6)
# A tibble: 16,347 × 4
  scientific_name       class   family        coords           
  <chr>                 <chr>   <chr>         <list>           
1 Aaaaba fossicollis    Insecta Buprestidae   <tibble [12 × 2]>
2 Aaaaba nodosus        Insecta Buprestidae   <tibble [16 × 2]>
3 Aades cultratus       Insecta Curculionidae <tibble [20 × 2]>
4 Aades griseatus       Insecta Curculionidae <tibble [5 × 2]> 
5 Aaroniella rawlingsi  Insecta Philotarsidae <tibble [35 × 2]>
6 Abantiades latipennis Insecta Hepialidae    <tibble [10 × 2]>
# … with 16,341 more rows

You can inspect elements in the list column like this:

inverts_nest |> 
  pluck("coords", 1) |>  # 1 refers to the first element of the list column
  print(n = 6)
# A tibble: 12 × 2
  longitude latitude
      <dbl>    <dbl>
1      153.    -28.4
2      151.    -33.8
3      153.    -31.0
4      146.    -37.8
5      153.    -30.3
6      151.    -33.8
# … with 6 more rows

The biggest change with working with list columns is that you have to iterate across each element. To do this, we will use various functions from the {purrr} package for the next calculation steps.

Species range overlap with fire-burned areas

Get fire layer

Shapefiles for the 2019-2020 fire season are available through the National Indicative Aggregated Fire Extent Dataset from the Department of Climate Change, Energy, the Environment and Water.

Click on Download Data (near the top of the page), then click on NIAFED_v20200623.zip to download the zip file. Save the zip file in your project folder and unzip to retrieve the shapefiles.

Now we can read the shapefile into R and set the projection to EPSG:4326. To speed up the computation of concave hulls and overlaps, we will remove elevation values and simplify the edges of the fire POLYGON.

fire <- st_read(here("your_directory_name", "NIAFED_20190701_20200622_v20200623.shp")) |> 
  st_transform(crs = 4326) |> 
  st_zm() |>  # Remove Z or M values
  ms_simplify() # Simplify edges of the fire layer

Choose taxonomic group

While it is possible to calculate range overlap with burned areas for all species in the dataset, it can take a lot of memory and processing time. Instead, we will demonstrate our workflow with — the bee family (Apidae) — as a working example.

# Filter invertebrate data to Apidae
bees <- inverts_nest |> 
  filter(family == "Apidae") 

Compute concave hull

In the next steps, we will work through the coordinate data for each species iteratively using map.

We will transform each species’ coordinates into an sf object using st_as_sf(), then compute the concave hulls with the concaveman() function. You can adjust the tightness of the hull boundary around a set of points using the concavity argument - the smaller the value, the tighter the hull. We’ve wrapped mutate() around these steps so the output will become variables in our tibble.

bees_concave <- bees |>
    mutate(points_sf = map(.x = coords,
                           ~ st_as_sf(.x, coords = c("longitude", "latitude"), # Set as sf object
                                      crs = 4326) |> 
                             rename(points = geometry)), # Rename geometry variable to something intuitive
           concave_sf = map(points_sf,
                            ~ concaveman(.x, concavity = 2) |> # Compute concave hulls
                              rename(concave = polygons)) # Rename geometry variable to something intuitive
           ) 

bees_concave |> print(n = 6)
# A tibble: 54 × 6
  scientific_name                   class   family coords   points_sf    conca…¹
  <chr>                             <chr>   <chr>  <list>   <list>       <list> 
1 Amegilla (Asaropoda) bombiformis  Insecta Apidae <tibble> <sf>         <sf>   
2 Amegilla (Asaropoda) calva        Insecta Apidae <tibble> <sf [5 × 1]> <sf>   
3 Amegilla (Asaropoda) dawsoni      Insecta Apidae <tibble> <sf>         <sf>   
4 Amegilla (Asaropoda) paracalva    Insecta Apidae <tibble> <sf [5 × 1]> <sf>   
5 Amegilla (Asaropoda) rhodoscymna  Insecta Apidae <tibble> <sf>         <sf>   
6 Amegilla (Notomegilla) aeruginosa Insecta Apidae <tibble> <sf>         <sf>   
# … with 48 more rows, and abbreviated variable name ¹​concave_sf

Compute range overlap and descriptive statistics

To compute range overlaps, we need to set our geometry calculations to assume the Earth is flat and not spherical by setting sf_use_s2(FALSE). This may be a limitation of the method but it still gives us a good approximation.

# Disable spherical geometry
sf_use_s2(FALSE) 

Using st_intersection(), we can identify the overlap between each species’ concave hull with fire-burned areas. We can then use st_area() to calculate the area (m2) of overlap and convert it into a percentage of each species’ original range so that all species are comparable.

Using possibly() with our map() functions allows the calculations to return NA for species that did not overlap with burned areas. Once calculations are complete, we will un-nest the variables: overlap_area and percent_overlap, so they appear as regular columns in our tibble.

# Calculate range overlap
bees_overlap <- bees_concave |>
  mutate(
    overlap_sf = map(concave_sf,
                     possibly(~ st_intersection(fire, .x) |> select(-Id) |> rename(overlap = geometry))), # Identify overlap
    overlap_area = map(overlap_sf,
                       possibly( ~ st_area(.x))), # Calculate area
    percent_overlap = map2(.x = overlap_area,
                           .y = concave_sf,
                           possibly( ~ (.x / st_area(.y)) * 100))) |> # Calculate percentage
  unnest(cols = c(overlap_area, percent_overlap)) # Unnest the area and percentage columns
    
bees_overlap |> print(n = 6)
# A tibble: 28 × 9
  scientific_name  class family coords   point…¹ conca…² overl…³ overl…⁴ perce…⁵
  <chr>            <chr> <chr>  <list>   <list>  <list>  <list>    [m^2]     [1]
1 Amegilla (Asaro… Inse… Apidae <tibble> <sf>    <sf>    <sf>    2.92e10   5.50 
2 Amegilla (Asaro… Inse… Apidae <tibble> <sf>    <sf>    <sf>    3.18e 8   0.138
3 Amegilla (Asaro… Inse… Apidae <tibble> <sf>    <sf>    <sf>    2.83e 8   0.277
4 Amegilla (Zonam… Inse… Apidae <tibble> <sf>    <sf>    <sf>    1.63e10   6.63 
5 Amegilla (Zonam… Inse… Apidae <tibble> <sf>    <sf>    <sf>    1.06e 7  15.4  
6 Amegilla (Zonam… Inse… Apidae <tibble> <sf>    <sf>    <sf>    1.10e11   3.08 
# … with 22 more rows, and abbreviated variable names ¹​points_sf, ²​concave_sf,
#   ³​overlap_sf, ⁴​overlap_area, ⁵​percent_overlap

Rank species by fire impact

Next, we will take the top 3 species with the highest percentage range overlap with fire-burned areas (percent_overlap) for our data visualisation. The top 3 species include a stingless bee, a reed bee, and a carpenter bee.

top3 <- bees_overlap |> 
  slice_max(order_by = percent_overlap,
            n = 3) 

top3
# A tibble: 3 × 9
  scientific_name  class family coords   point…¹ conca…² overl…³ overl…⁴ perce…⁵
  <chr>            <chr> <chr>  <list>   <list>  <list>  <list>    [m^2]     [1]
1 Austroplebeia c… Inse… Apidae <tibble> <sf>    <sf>    <sf>    5.48e 8    37.1
2 Exoneura (Exone… Inse… Apidae <tibble> <sf>    <sf>    <sf>    2.08e10    28.2
3 Xylocopa (Kopto… Inse… Apidae <tibble> <sf>    <sf>    <sf>    9.92e 9    27.1
# … with abbreviated variable names ¹​points_sf, ²​concave_sf, ³​overlap_sf,
#   ⁴​overlap_area, ⁵​percent_overlap

Make map

We will now select the variables we need and un-nest the relevant ones (points_sf, concave_sf, overlap_sf) for plotting - this gives us everything we need to create our maps!

bee_map_data <- top3 |> 
  select(scientific_name, points_sf, concave_sf, overlap_sf) |> 
  unnest(cols = c(points_sf, concave_sf, overlap_sf)) 

bee_map_data |> print(n = 6)
# A tibble: 31 × 4
  scientific_name                points                                  concave
  <chr>                     <POINT [°]>                            <POLYGON [°]>
1 Austroplebeia cassiae (143.45 -14.08) ((143.27 -14.08, 143.28 -14.05, 142.92 …
2 Austroplebeia cassiae (143.27 -14.08) ((143.27 -14.08, 143.28 -14.05, 142.92 …
3 Austroplebeia cassiae  (143.32 -14.1) ((143.27 -14.08, 143.28 -14.05, 142.92 …
4 Austroplebeia cassiae (145.13 -15.67) ((143.27 -14.08, 143.28 -14.05, 142.92 …
5 Austroplebeia cassiae (143.28 -14.05) ((143.27 -14.08, 143.28 -14.05, 142.92 …
6 Austroplebeia cassiae (142.92 -13.42) ((143.27 -14.08, 143.28 -14.05, 142.92 …
# … with 25 more rows, and 1 more variable: overlap <MULTIPOLYGON [°]>

Create the base map

Let’s create our base map with the outline of Australia and the fire-burned area. You can see a majority of burnt areas are located in Northern Australia and the South-East coast.

# Retrieve Australia polygon
aus <- st_transform(ozmap_country, 4326)

base_map <- ggplot() + 
  geom_sf(data = aus, colour = "black", fill = "white") +
  geom_sf(data = fire, fill = "#FEC3A6", colour = "#FEC3A6") + 
  theme_void()

Add species range overlap

Now we can add the range overlap of our three bee species. We use geometry within aes to specify which variable from bee_map_data we want to plot.

Create inset map

The navy blue hull in the top right corner of Australia is very small (Austroplebeia cassiae), so we will make an enlarged inset map so we can see it clearer.

inset <- main_map + 
   coord_sf(
    xlim = c(142.8 , 145.3),
    ylim = c(-15.9, -13.25),
    expand = FALSE
            ) + 
   theme_void() +
   theme(legend.position = "none",
         panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.2))

We’ll also draw a box around the area of interest in the enlarged map.

main_bbox <- main_map + 
   geom_rect(aes(xmin = 142.8, xmax = 145.3,
             ymin = -15.9, ymax = -13.25),
             colour = "black",
             fill = NA, 
             lwd = 0.2) 

Arrange map components

Finally, we can arrange our base map and inset together for our final map!

This map shows the three bee species with the highest percentage overlap with fire-burned areas. Two of these bee species are located in Northern Australia and one is located in South-Eastern Australia.

combined_map <- ggdraw(main_bbox) +
  draw_plot(inset, 
            x = 0.52, y = 0.63, 
            width =0.45, height = 0.3)

combined_map

Bonus: Vascular plants

We repeated the same workflow with the vascular plant dataset and created a map of range overlap with burned areas for the genus Daviesia.

Commonly known as bitterpeas, Daviesia comprises plants pollinated by reed bees (Exoneura), which are featured in the bee map above.

Code
# Extract candidate genus
daviesia <- vplant_nest |> 
  filter(genus == "Daviesia")

# Compute concave hulls
daviesia_concave <- daviesia |>
    mutate(points_sf = map(.x = coords,
                           ~ st_as_sf(.x, coords = c("longitude", "latitude"),
                                      crs = 4326)), 
           concave_sf = map(points_sf,
                            ~ concaveman(.x)))

# Compute range overlap and descriptive statistics and select 
daviesia_overlap <- daviesia_concave |> 
  mutate(overlap_sf = map(concave_sf, 
                          possibly(~ st_intersection(fire, .x) |> select(-Id))),
  overlap_area = map(overlap_sf,
                     possibly(~ st_area(.x))),
  percent_overlap = map2(.x = overlap_area,
                         .y = concave_sf,
                         possibly(~ (.x / st_area(.y))*100))) |> 
  unnest(cols = c(overlap_area, percent_overlap)) 

## Prepare for plotting and rename variables
daviesia_map_data <- daviesia_overlap |> 
  select(species, overlap_area, percent_overlap, points_sf, concave_sf, overlap_sf) |> 
  unnest() |> 
  rename(points = geometry, 
         concave = polygons, 
         overlap = geometry1) 

## Create main map reusing base_map from above
daviesia_main_map <- base_map + 
    geom_sf(data = daviesia_map_data, 
          aes(geometry = concave, 
              colour = species, 
              fill = species), 
          size = 1.5, alpha = 0.005) + 
  geom_sf(data = daviesia_map_data, 
          aes(geometry = overlap), 
          colour = "#FF925C", fill = "#FF925C") + 
  geom_sf(data = daviesia_map_data, 
          aes(geometry = points, 
              colour = species), 
          size = 0.9) + 
  scale_colour_manual(values = c("#023E50", "#7B8A6A", "#3C908E" )) + 
  scale_fill_manual(values = c("#023E50", "#7B8A6A", "#3C908E")) + 
  guides(colour = guide_legend(override.aes = list(alpha = 1))) + 
  coord_sf(xlim = c(140, 158),
           ylim = c(-23, -43)) +
  theme(legend.title= element_blank(),
        legend.position = "bottom") 

# Inset 1
daviesia_inset_1 <- daviesia_main_map +
coord_sf(
    xlim = c(145.5 , 150.3),
    ylim = c(-35, -37.95),
    expand = FALSE
  ) + 
  theme_void() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3))

# Inset 2
daviesia_inset_2 <- daviesia_main_map +
  coord_sf(
    xlim = c(151.55 , 152.75),
    ylim = c(-28.6, -31.25),
    expand = FALSE
  ) + 
  theme_void() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "black", fill = NA, linewidth = 0.3))

# Drawing the inset boxes on main map
daviesia_bbox <- daviesia_main_map + 
   geom_rect(aes(xmin = 145.5, xmax = 150.3, # Inset 1
             ymin = -35, ymax = -37.95),
             colour = "black",
             fill = NA, linewidth = 0.2) + 
  geom_rect(aes(xmin = 151.55, xmax = 152.75, # Inset 2
             ymin = -28.6, ymax = -31.25),
             colour = "black",
             fill = NA, linewidth = 0.2) 

# Daviesia plot with insets 
daviesia_combined <- ggdraw(daviesia_bbox) +
  draw_plot(daviesia_inset_1, x = 0.59, y = 0.15, 
            width = 0.42, height = 0.30) +
  draw_plot(daviesia_inset_2, x = 0.52, y = 0.52, 
            width = 0.5, height = 0.4)

daviesia_combined

Final thoughts

In natural catastrophes, decision makers have limited time to act. They need ready-to-go data and workflows to assess and manage possible consequences of the catastrophe and any proposed ways to mitigate it. Here, we used curated datasets of Australian invertebrates and vascular plants to illustrate how concave hulls can represent estimate species range and estimate range overlap with natural disasters. We hope our work can aid future assessments of vulnerable species and help prioritise conservation efforts.

Acknowledgement:

The work in this post is part of a project titled: Curated biodiversity data for rapid assessment of bushfire impact. This project is funded by the Australian Research Data Commons (ARDC) bushfire data challenges program.

Expand for session info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.2 (2022-10-31 ucrt)
 os       Windows 10 x64 (build 19044)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  English_Australia.utf8
 ctype    English_Australia.utf8
 tz       Australia/Sydney
 date     2023-04-13
 pandoc   2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package        * version date (UTC) lib source
 concaveman     * 1.1.0   2020-05-11 [1] CRAN (R 4.2.2)
 cowplot        * 1.1.1   2020-12-30 [1] CRAN (R 4.2.1)
 dplyr          * 1.1.0   2023-01-29 [1] CRAN (R 4.2.2)
 forcats        * 1.0.0   2023-01-29 [1] CRAN (R 4.2.2)
 ggplot2        * 3.4.1   2023-02-10 [1] CRAN (R 4.2.2)
 ggpointdensity * 0.1.0   2019-08-28 [1] CRAN (R 4.2.1)
 gt             * 0.9.0   2023-03-31 [1] CRAN (R 4.2.3)
 here           * 1.0.1   2020-12-13 [1] CRAN (R 4.2.1)
 htmltools      * 0.5.4   2022-12-07 [1] CRAN (R 4.2.2)
 lubridate      * 1.9.2   2023-02-10 [1] CRAN (R 4.2.2)
 ozmaps         * 0.4.5   2021-08-03 [1] CRAN (R 4.2.1)
 patchwork      * 1.1.2   2022-08-19 [1] CRAN (R 4.2.1)
 purrr          * 1.0.1   2023-01-10 [1] CRAN (R 4.2.2)
 readr          * 2.1.4   2023-02-10 [1] CRAN (R 4.2.2)
 rmapshaper     * 0.4.6   2022-05-10 [1] CRAN (R 4.2.1)
 sessioninfo    * 1.2.2   2021-12-06 [1] CRAN (R 4.2.1)
 sf             * 1.0-9   2022-11-08 [1] CRAN (R 4.2.2)
 stringr        * 1.5.0   2022-12-02 [1] CRAN (R 4.2.2)
 tibble         * 3.1.8   2022-07-22 [1] CRAN (R 4.2.1)
 tidyr          * 1.3.0   2023-01-24 [1] CRAN (R 4.2.2)
 tidyverse      * 2.0.0   2023-02-22 [1] CRAN (R 4.2.2)
 viridis        * 0.6.2   2021-10-13 [1] CRAN (R 4.2.1)
 viridisLite    * 0.4.1   2022-08-22 [1] CRAN (R 4.2.1)

 [1] C:/Users/KEL329/R-packages
 [2] C:/Users/KEL329/AppData/Local/Programs/R/R-4.2.2/library

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